Search

Wildfire is a dominant disturbance agent in pan‐Arctic tundra and can significantly impact terrestrial carbon balance and ecosystem functioning. Interactions between fire and climate change can enhance their impacts on the Arctic. However, the driving mechanisms of tundra fire occurrence remain poorly understood. This study focuses on identifying key environmental factors controlling fire occurrence in Arctic tundra of Alaska. Our random forest models, considering ignition source, fuel, fire weather, and topography, have shown a strong predictive capability with an overall accuracy above 91%. We found cloud‐to‐ground (CG) lightning to be the dominant driver controlling tundra fire occurrence. Near‐surface weather warmer and drier than normal was required to support burning, while fuel composition and topography have modest correlations with fire occurrence. Our results highlight the critical role of CG lightning in driving tundra fires and that incorporating lightning in modeling is essential for fire monitoring, forecasting, and management in the Arctic. Plain Language Summary: Tundra fires can exert a considerable influence on the local ecosystem functioning and contribute to climate change. However, the drivers and mechanisms of tundra fires are still poorly understood. Research on modeling contemporary fire occurrence in the tundra is also lacking. Here we examined the key environmental factors driving tundra fire occurrence with numeric weather prediction and statistical models. We found that tundra fire occurrence is primarily controlled by cloud‐to‐ground lightning. Warmer and drier fire weather conditions also support burning in the tundra. We recommend the integration of lightning modeling for fire monitoring and forecasting in the data‐scarce regions like the Arctic. Key Points: Cloud‐to‐ground lightning probability is the key driver of fire occurrence in Arctic tundraWarmer and drier near‐surface fire weather conditions also promote tundra firesAn empirical‐dynamic framework combining Weather Research and Forecast (WRF) and statistical learning methods shows promise for modeling tundra fire occurrence [ABSTRACT FROM AUTHOR]

The impact of the Pacific multi‐decadal oscillation (PMO) on precipitation in April during the sowing season in Northeast China (NEC) was analysed. According to the results, there was a significant inter‐decadal change in the relationship between the PMO in December of the previous year and the precipitation in NEC in April of the following year during different PMO phases. Before 1980, when the PMO was in a negative phase, there was no significant correlation between the PMO and NEC April precipitation. After 1980, when the PMO changed to a positive phase, the relationship between the two variables became significant. Based on further analysis, the main reason for this relationship is that as the PMO changes from a negative phase to a positive phase, the northern North Pacific warms, and the north–south temperature gradient in the mid‐ to high latitudes of the North Pacific decreases, which weakens the mid‐ to high‐level westerlies, thus increasing the impact of North Pacific water vapour transport on the April precipitation in NEC. When the PMO changes from a negative phase to a positive phase, the increased sea surface temperature (SST) significantly strengthens the Aleutian low (AL), which can enhance the Alaska warm current and further increase the sea temperature in the PMO region. This air–sea interaction forms a positive feedback that maintains the SST signal from December of the previous year to April of the following year, which affects the circulation and water vapour conditions of NEC and ultimately affects NEC April precipitation. This positive feedback mechanism was further verified in the ECHAM5 model. When the positive anomalous SST appeared in the PMO region, as in the observations after 1980, the AL was significantly enhanced, which in turn affected the SST enhancement. [ABSTRACT FROM AUTHOR]

Satellite imagery has been widely used for the assessment of wildfire burn severity within the scientific community and fire management agencies. Multiple indices have been proposed to assess burn severity, among which the differenced Normalized Burn Ratio (dNBR) is arguably the most commonly used index that is expected to provide an objective and consistent assessment. However, although evidence of variability in the dNBR-based assessment of burn severity driven by image pair selection has been shown in many studies, the comprehensive examination of the extent of the bias resulting from the image selection has been lacking. In this study, we focus on three factors of the image selection process which are encountered by most Landsat-derived dNBR applications, including the sensor combination and the difference in timing of image acquisition (for both the year and seasonality) of pre- and post-fire image pairs. Through separate analyses, each targeting a single factor, we show that Landsat sensor combination between the pre- and post-fire images has a limited impact on the dNBR values. The difference in the year of acquisition between the images in the image pairs is shown to influence dNBR assessment with a noticeable increase in mean dNBR (>0.1) with only a single year difference between images compared to multi-year differences. However, differences in the image acquisition seasons and the resulting phenological differences is shown to impact dNBR values most considerably. Based on our results, we warn against the calculation of dNBR when the images are acquired in different seasons. We believe that despite the existence of multiple derivatives of dNBR, there remains a need for an improved version; one that is less susceptible to the phenological impacts introduced by the selected images. [ABSTRACT FROM AUTHOR]

Satellite remote sensing has been widely used for the evaluation of wildfire burn severity in various ecosystems. While a variety of remote sensing-based burn severity indices have been developed, the Landsat-based differenced Normalized Burn Ratio (dNBR) presents the most widely-used approach to burn severity assessment for fire research and management. Although dNBR-based approaches have been continuously updated, including the development of the relative dNBR (RdNBR) and the Relativized Burn Ratio (RBR), one key obstacle for the reliable applications of the burn severity indices in the high northern latitudes has not been adequately addressed. Specifically, optimal Landsat image pairs are very rarely available for the calculation of the burn severity indices in certain regions of the high northern latitudes (HNL), resulting in the burn severity index outputs calculated for a large number of wildfires being considerably affected by non-fire-related factors. The suboptimal selection of image pairs may have partially contributed to the lack of consistency in the performance of the burn severity indices in the HNL regions. In this paper, we systematically evaluated the impacts of suboptimal image pairs on signal stability through two sets of analyses conducted at different spatial scales. First, at the regional scale, we examined the burn severity indices calculated for a selection of wildfires in Alaska. Here, we demonstrate the inconsistent performance of the same index calculated based on Landsat data that are typically considered as "optimal". In addition, we show that the calculated indices may be more prone to the negative impacts of these inconsistencies, thus leading to reduced reliability, when there is limited Landsat data availability. Second, at the continental scale, we show that certain areas in the HNL, especially Alaska during the pre-2000 era, are subject to the potentially strong negative impact associated with the limited data availability. Through systematically analyzing this issue, we hope to not only divert more attention to it but also to provide potential solutions, based on which further improvements that may be of particular importance for research on Arctic wildfires could be made. • Examines a limitation of burn severity indices that is specific to the Arctic region • Takes advantage both local and cloud-based platforms for more robust analyses • Utilizes full Landsat archive over North America in the spatio-temporal analysis • Offers potential solutions to address the revealed limitation [ABSTRACT FROM AUTHOR]